Method, system, and computer readable medium for generating pulse oximetry predictive scores (pops) for predicting adverse outcomes in preterm infants

ABSTRACT

A method and system for generating pulse oximetry predictive scores for predicting adverse outcomes in preterm infants.

RELATED APPLICATION

This is a Continuation-In-Part (CIP) application of PCT Application No.PCT/US17/30606, filed on May 2, 2017, which claims the benefit of U.S.Provisional Application No. 62/330,463, filed on May 2, 2016.

GOVERNMENT SUPPORT CLAUSE

This invention was made with government support under Grant No. HD072071awarded by the National Institutes of Health. The government has certainrights in the invention.

FIELD

The present invention is directed to a method, system, and computerreadable medium for generating pulse oximetry predictive scores forpredicting adverse outcomes in preterm infants.

BACKGROUND

Pulse oximetry is universally used to monitor all infants in theNeonatal Intensive Care Unit (NICU), from birth until discharge. Thereis an abundance of important physiologic information in the pulseoximetry signal, and clinicians use only a fraction of this data.Abnormal patterns of heart rate or oxygenation can indicate risk foradverse events or outcomes occurring in the near or far term.

There exists a need for generating pulse oximetry predictive scores(POPS) for predicting adverse outcomes in preterm infants. The pulseoximetry predictive scores (POPS) can be used to: 1) identifyhighest-risk infants for additional surveillance or therapeuticinterventions that might not be appropriate for all preterm infants; 2)stratify infants for clinical trials based on risk profiles; 3) provideearly warning system for late-onset sepsis and necrotizing enterocolitis(NEC); and 4) earlier diagnosis and treatment to improve outcomes.

SUMMARY

The presently described subject matter is directed to an improvedmethod, system, and apparatus for pulse oximetry predictive scores forpredicting adverse outcomes in preterm infants.

The presently described subject matter is directed to a method forgenerating pulse oximetry predictive scores for predicting adverseoutcomes in preterm infants, comprising or consisting of a predictivealgorithm.

The presently described subject matter is directed to a method forgenerating pulse oximetry predictive scores for predicting adverseoutcomes in preterm infants, comprising or consisting essentially of orconsisting of a predictive algorithm; and displaying the resultingpredictive scores.

The presently described subject matter is directed to a method forgenerating pulse oximetry predictive scores for predicting adverseoutcomes in preterm infants, comprising or consisting essentially of orconsisting of: measuring mean and standard deviation of heart rate (HR)and oxygen saturation (SpO2); measuring cross-correlation of HR andSpO2; measuring HR decelerations; measuring HR and SpO2 entropy;measuring hypoxia and hyperoxia; analyzing the measured data; andgenerating pulse oximetry predictive scores using the analyzed data,wherein these measurements are made over different time periods specificto a pathology being predicted.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, wherein the time periods are based on death orintraventricular hemorrhage (IVH) at first 24 hour or shorter timeperiods, after birth.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, wherein the time periods are based on sepsis or NEC at2 days leading up to clinical diagnosis, including increase over patientbaseline or increase over population normal.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, wherein the time periods are based on bronchopulmonarydysplasia (BPD) or retinopathy of prematurity (ROP) at first day, week,or month.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, wherein the time periods are based on prolongedNeonatal Intensive Care Unit (NICU) stay at first day, week, or month.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, including generating an algorithm using gestationalage, birth weight, and post-menstrual age.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, including exploring specific laboratory values,including white blood cell count, hematocrit, and C-reactive protein.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, including developing predictive scores using pulseoximeter-derived heart rate (HR) and oxygen saturation (SpO2).

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, including developing predictive scores using pulseoximeter-derived heart rate (HR) and oxygen saturation (SpO2), andincluding using standard demographic risk factors to determine risk ofearly or late death or prolonged NICU stay.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, including monitoring changes in cardiorespiratorychanges occurring in the preterm infants with systemic inflammationrelated to late-onset septicemia (LOS) or necrotizing enterocolitis(NEC), and alerting clinicians before overt signs of illness emerges.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, including monitoring changes in cardiorespiratorychanges occurring in the preterm infants with systemic inflammationrelated to late-onset septicemia (LOS) or necrotizing enterocolitis(NEC), and alerting clinicians before overt signs of illness emerges,and further including monitoring respiratory rate (RR), heart rate (HR),and oxygen saturation (SpO2) of the preterm infants.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, including monitoring changes in vital sign patterns ofthe preterm infants.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, including monitoring changes in vital sign patterns ofthe preterm infants, wherein the monitoring data, includes respirationrate (RR) is derived from the chest impedance signal, heart rate (HR) isderived from an electrocardiogram (ECG) signal, and SpO2 is derived froma pulse oximeter, and the monitoring data is collected every 2 seconds.

The presently described subject matter is directed to the above methodfor pulse oximetry predictive scores for predicting adverse outcomes inpreterm infants, including monitoring changes in vital sign patterns ofthe preterm infants, wherein the monitoring data, includes respirationrate (RR) is derived from the chest impedance signal, heart rate (HR) isderived from an electrocardiogram (ECG) signal, and SpO2 is derived froma pulse oximeter, and the monitoring data is collected every 2 secondsand further wherein maximum cross-correlation between two vital signsignals is measured over ten-minute windows by first standardizing eachsignal and then using a Matlab function XCORR, with a lag time of −30 to+30 seconds.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a table of pulse oximetry data showing death rates.

FIG. 2 is a diagrammatic view showing the relative risk score forvarious time periods.

FIG. 3 is a table of vital signs and their cross-correlation over theentire NICU stay and in the 24^(th) period prior to LOS or NEC events.

FIG. 4 is a table of site-specific vital sign and model performance forLOS and NEC detection.

FIG. 5 show graphs of center-specific vital sign model ability todiscriminate infants with impending LOS or NEC.

FIG. 6 are graphs of the distribution of cross-correlation of HR-SpO2measurement skewed toward higher values in the 24^(th) period prior toLOS or NEC diagnosis compared to the values of all infants at all times.

FIG. 7 show graphs of the rise in cross correlation HR-SpO2 beinggreater in cases of NEC than LOS, and continued to rise after diagnosis.

FIG. 8 show graphs of the effect as seen in infants at bothinstitutions.

FIGS. 9A-H show tables of effect in infants on or off mechanicalventilation at the time of diagnosis.

FIG. 10 is a block diagram illustrating an example of a machine uponwhich one or more aspects of embodiments of the present invention can beimplemented.

FIG. 11 is a diagram illustrating Pulse Oximetry Warning Score “POWS”for Sepsis and NEC Prediction in VLBW Infants at 3 NICUs.

FIG. 12 is a diagram illustrating Heart Rate and Oxygen SaturationCross-Correlation in Preterm Infants: Association with Apnea and AdverseEvents.

FIG. 13 is a diagram illustrating Pulse oximetry data predict risk ofdeath and adverse outcomes in VLBW infants.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention is directed to a method, system, and computerreadable medium for generating pulse oximetry predictive scores forpredicting adverse outcomes in preterm infants.

The pulse oximetry predictive scores (POPS) can be used to: 1) identifyhighest-risk infants for additional surveillance or therapeuticinterventions that might not be appropriate for all preterm infants; 2)stratify infants for clinical trials based on risk profiles; 3) provideearly warning system for late-onset sepsis and NEC; and 4) earlierdiagnosis and treatment to improve outcomes.

The present invention includes:

-   1) analyzing archived pulse oximetry data and carefully annotated    and audited clinical data from ˜1000 preterm infants in the UVA NICU    from 2009-2015; and-   2) developing pulse oximetry-based algorithms to predict:    -   a) death (early <3 days or late >=3 days);    -   b) intraventricular hemorrhage (this generally occurs within the        first 3 days after birth, is associated with adverse        neurodevelopmental outcomes);    -   c) sepsis (early <3 days or late >=3 days blood culture-positive        sepsis);    -   d) necrotizing enterocolitis (NEC);    -   e) bronchopulmonary dysplasia (BPD) or chronic lung disease        which is associated with prolonged length of NICU stay and        long-term respiratory and neurologic morbidity);    -   f) retinopathy of prematurity (ROP, retinal vasculopathy with        risk for blindness); and    -   g) prolonged NICU stay (which is costly).

The measurements according to the present invention include:

-   -   a) mean and standard deviation of HR and SpO2;    -   b) cross-correlation of HR and SpO2;    -   c) HR decelerations;    -   d) HR and SpO2 entropy; and    -   e) hypoxia and hyperoxia measurements including hypoxia index        (area under curve of various SpO2 thresholds, 88%, 85%, 80%,        75%), number of hypoxia or hyperoxia events, “delta SpO2” or        depth and frequency of acute decrease/increase in SpO2.

These measurements will be made over different time periods specific tothe pathology being predicted. For example:

-   -   1) death or IVH in first 24 hour or shorter time period after        birth;    -   2) sepsis or NEC in 2 days leading up to clinical diagnosis        (increase over patient baseline or increase over population        normal);    -   3) BPD or ROP in first day, week, month. For both, it is        possible that hyperoxia and hypoxia have time windows of        susceptibility to organ damage, which will be evaluated; and    -   4) prolonged NICU stay in first day, week, or month.

The present invention can determine the additive value of variables suchas gestational age, birth weight, and post-menstrual age into thealgorithms.

The present invention can explore the additive value of specificlaboratory values (or changes in laboratory values) such as white bloodcell count, hematocrit, C-reactive protein.

The present invention can advise on methodology, including both basicmodeling (e.g. logistic regression models) and methods such as randomforest, decision tree, machine learning.

The present invention includes analysis of archived data at bothUniversity of Virginia (UVA) and Columbia University (CU) for modeldevelopment, and alternative training/testing data sets to assure modelswork across institutions. The present invention can include a third NICUwith high-volume/high acuity to develop and validate the models.

The pulse oximetry predictive scores (POPS) can be used to:

-   -   1) identify highest-risk infants for additional surveillance or        therapeutic interventions that might not be appropriate for all        preterm infants;    -   2) stratify infants for clinical trials based on risk profiles;        and    -   3) provide an early warning method or system for late-onset        sepsis and NEC to provide earlier diagnosis and treatment likely        leading to improved outcomes.

The bedside display of pulse oximetry predictive scores (POPS) canfacilitate the early detection of sepsis/NEC. Further, the pulseoximetry data within 3 hours of birth can be used to predict death andprolonged NICU stays for preterm infants. In addition, predictingadverse events in preterm infants is useful for risk stratification inclinical trials and quality improvement initiatives.

To develop pulse oximetry predictive scores (POPS) using pulseoximeter-derived heart rate (HR) and oxygen saturation (SpO2) from theday of birth can add to standard demographic risk factors to determinerelative risk of early or late death or prolonged NICU stay.

For all infants <35 weeks gestational age (GA) admitted at birth to theUVA NICU from 2009-2015, pulse oximeter data was analyzed within 3 hrsof birth. The relative risk of mortality was estimated using aparsimonious logistic regression model with variables of gestational age(GA), birth weight (BW), and mean SpO2 obtained using a stepwiseselection process. In a separate analysis using random forests,significant variables identified in order of importance included meanSPO2, percent SPO2<85, BW, standard deviation of SPO2, percent SPO2<80,and standard deviation of HR.

The score generated from this model was evaluated for discriminatingadverse outcomes including early (“3d) and late (>3d) death andprolonged NICU stay (beyond 40 week post-menstrual age).

Pulse oximetry data was available in the first 3 hours after birth for930 infants, of which 35 died, as shown in FIG. 1). Mean SpO2 addedsignificantly to gestational age (GA) and birth weight (BW) to predictoverall mortality (AUC 0.850, change in AUC 0.146, p<0.0001). Therelative risk score predicted early compared to late death and prolongedNICU stay, as shown in FIG. 2.

The average oxygen saturation (SpO2) obtained from pulse oximetry in thefirst 3 hours after birth predicts early and late death and olderpostmenstrual age at discharge in preterm infants and adds to thegestational age and birth weight for risk prediction.

Cardiorespiratory changes occur in preterm infants with systemicinflammation related to late-onset septicemia (LOS) or necrotizingenterocolitis (NEC), and alerting clinicians to these changes beforeovert signs of illness emerge can lead to earlier treatments andimproved outcomes.

Identifying changes in vital signs and their cross-correlation prior toLOS or NEC diagnosis in very low birth weight (VLBW) infants wasconducted in two Neonatal Intensive Care Units (NICUs). The analysis ofbedside monitor cardiorespiratory and clinical data was used.

The NICUs at the University of Virginia and Columbia University MedicalCenter. The participants include 1065 preterm very low birth weight(VLBW) infants.

The respiratory rate (RR), heart rate (HR), and oxygen saturation (SpO2)were collected every 2 seconds from the bedside monitors for the entireNICU stay (131 infant-years' data). The mean, standard deviation (SD),and cross-correlation of the vital signs over 10 minute windows averagedeach hour were analyzed at all times that data was available resultingin 1.15M measurements, including within 1 day of 186 episodes of LOS orNEC. The vital sign and demographic models were evaluated for ability topredict illness within 24 hours, and the results were compared to heartrate characteristics index previously validated for sepsis detection.

The cross-correlation of HR-SpO2 was the best single measure for eitherLOS or NEC detection and remained highly significant (p<0.00001) whenadjusted for postmenstrual age which was the best demographic predictor(combined ROC area 0.733). A 3-variable model (cross correlation ofHR-SpO2, mean SpO2, and SD HR) increased the ROC area by 0.021 over anestablished heart rate characteristics index for illness prediction (NetReclassification Improvement 0.25, 95% CI 0.113, 0.328). The modelperformance differed between the two (2) NICUs, but remained highlysignificant when internally and externally validated. The 3-variablemodel trained at UVA had an internal ROC area of 0.695 and an externalROC area of 0.754. The same model trained at Columbia had internal andexternal ROC areas of 0.745 and 0.680, respectively.

Despite minor inter-institutional differences in vital sign patterns ofVLBW infants, cross-correlation of HR-SpO2 and a 3-variable vital signmodel performed well at both centers for preclinical detection of sepsisor NEC.

The analysis of changes in vital sign patterns in hospitalized patientscan yield important information about impending clinical deteriorationand might alert clinicians before they would otherwise recognize signsof illness (See Lake D E, Fairchild K D, Moorman J R. Complex signalsbioinformatics: evaluation of heart rate characteristics monitoring as anovel risk marker for neonatal sepsis. J Clin Monit Comput2013;28:329-39; Mithal L B, Yogev R, Palac H, Gur I, Mestan K K.Computerized vital signs analysis and late onset infections in extremelylow gestational age infants. J Perinat Med 2016; and Bravi A, Green G,Longtin A A, Seely A J E. Monitoring and Identification of SepsisDevelopment through a Composite Measure of Heart Rate Variability. PLoSOne 2012; 7:e45666).

The present inventors previously developed a system for analyzing heartrate characteristics in infants in the neonatal intensive care unit(NICU) that identifies decreased heart rate variability anddecelerations that occur prior to diagnosis of sepsis (See Moorman J R,Carlo W A, Kattwinkel J, et al. Mortality reduction by heart ratecharacteristic monitoring in very low birth weight neonates: Arandomized trial. J Pediatr 2011;159; Griffin M P, O'Shea T M,Bissonette EA, Harrell F E, Lake D E, Moorman J R, Abnormal heart ratecharacteristics preceding neonatal sepsis and sepsis-like illness.Pediatr Res 2003;53:920-6; and Griffin M P, Lake D E, Bissonette E A,Harrell F E, O'Shea T M, Moorman J R, Heart rate characteristics: novelphysiomarkers to predict neonatal infection and death. Pediatrics2005;116:1070-4).

The displaying a heart rate characteristics score to clinicians loweredsepsis-associated mortality 40% in a large randomized clinical trial ofvery low birth weight (VLBW) infants (See Fairchild K D, Schelonka R L,Kaufman D a, et al. Septicemia mortality reduction in neonates in aheart rate characteristics monitoring trial. Pediatr Res 2013;74:570-5).While changes in heart rate patterns provide some information aboutcardiovascular stability and autonomic nervous system activation anddysfunction, changes in other vital signs that occur during a systemicinflammatory response can be exploited for predictive monitoring (SeeFairchild KD. Predictive monitoring for early detection of sepsis inneonatal ICU patients. Curr Opin Pediatr 2013;25:172-9; and Sullivan BA, Fairchild K D. Predictive monitoring for sepsis and necrotizingenterocolitis to prevent shock. Semin Fetal Neonatal Med2015;20:255-61).

The acute illness in preterm infants is often associated with increasedfrequency or severity of central apnea associated with bradycardia andoxygen desaturation (“ABD” events). The present invention can use anautomated algorithm that analyzes waveform and vital sign data from NICUbedside monitors to show that ABD events and periodic breathing increasein the day prior to diagnosis in some infants with septicemia ornecrotizing enterocolitis (NEC) (See Patel M, Mohr M, Lake D, et al.Clinical Associations with Immature Breathing in Preterm Infants. Part2: Periodic Breathing. Pediatr Res 2016; and Fairchild K, Mohr M,Paget-Brown A, et al. Clinical associations of immature breathing inpreterm infants: part 1-central apnea. Pediatr Res 2016). Waveform dataare generally sampled at high frequency by standard ICU monitors (in ourunits, chest impedance at 60 Hz and 3 leads of ECG at 240 Hz each), andtherefore analysis of central apnea requires very large data storage andprocessing capabilities not available at most centers.

The present invention sought to develop simpler methods for analyzingvital sign values and their interactions to predict acute illness. Thepresent invention focused on vital signs collected every 2 seconds (0.5Hz) from bedside monitors: heart rate (HR), respiratory rate (RR), andoxygen saturation from pulse oximetry (SpO2). In a preliminary analysisof infants in a single NICU, it was found that increasedcross-correlation (or trending together, allowing for a lag) of HR andSpO2 performed well for preclinical detection of sepsis (Moss,Fairchild, Lake, Moorman accepted for publication, Critical CareMedicine, March 2016). Some of this increased cross-correlation likelyrepresents changes in HR and SpO2 occurring in synchrony with pauses inbreathing.

In the present invention, the study was expanded on this finding byanalyzing vital signs from a large number of VLBW infants in two NICUs,both at baseline and surrounding two illnesses, late-onset septicemia(LOS) and NEC.

The present invention collected and stored all bedside monitor vitalsign data on all patients admitted to the University of Virginia (UVA)NICU over a 64 month period from 2009 to 2015 and to the Children'sHospital of New York NICU (Columbia University, CU) over an 18 monthperiod from 2013 to 2015. All VLBW infants with data available wereincluded in this study, which was approved by the Institutional ReviewBoards of both institutions with waiver of consent due to its purelyobservational nature.

The clinical data was abstracted from electronic medical records into arelational clinical database. The demographics included gestational age,birth weight, gender, and final outcome (death, discharge, or transfer).The cases of LOS and NEC were identified from review of clinicaldatabases. LOS was defined as signs of sepsis and a positive bloodculture at 3 or more days of age and at least 5 days of treatment withantibiotics. The subsequent episodes of LOS or NEC were included if theyoccurred more than 7 days after the previous episode. NEC is defined asclinical and radiographic signs of NEC and a full course of therapy(bowel rest and antibiotics). The present invention excluded cases offocal intestinal perforation without NEC as identified by the attendingneonatologist and pediatric surgeon, based on clinical and, whenavailable, surgical findings. The cases in which infants weretransferred from an outside hospital with LOS or NEC were excluded sincebaseline “well” data were not available for comparison.

The data collected related to LOS or NEC episodes included chronologicand post-menstrual age at the time of the blood culture or abdominalradiograph establishing the diagnosis, blood culture results, andwhether on ventilatory support at the time of diagnosis.

The bedside monitor data was collected using a BedMaster central networkserver (Excel Medical, Jupiter, Fla.). RR derived from the chestimpedance signal, HR from the ECG signal, and SpO2 from the pulseoximeter were collected every 2 seconds. During the time period of studyat both institutions, pulse oximeters were set to the default SpO2averaging setting (8 seconds). The mean, standard deviation, andcross-correlation of HR, RR, and SpO2 were calculated in 10 minutewindows and then averaged for each hour for analysis throughout the NICUstay and then specifically in the 2 day period before and afterdiagnosis of LOS and NEC.

The maximum cross-correlation between two vital sign signals wasmeasured over ten-minute windows by first standardizing each signal(subtracting mean and dividing by standard deviation) and then using theMatlab function XCORR, with a lag time of −30 to +30 seconds. A highvalue of this statistic (approaching 1) indicates the two signals are inpositive synchrony (i.e. they go up and down together) with a possiblelag or time difference of up to 30 seconds. The present inventors alsocalculated the minimum cross-correlation value reflecting signals goingin opposite directions; these negative synchrony values are not reportedbecause they were not associated with adverse events.

At UVA, a heart rate characteristics (HRC) index monitor has been in usesince 2003 (HeRO monitor, Medical Predictive Science Corporation,Charlottesville, Va.). The monitor was developed as an early warningsystem for sepsis, and the HRC index incorporates three measures ofabnormal HR characteristics that occur in some neonates with sepsis: lowHR variability, sample asymmetry (more decelerations, feweraccelerations), and low sample entropy. The HRC index is displayed atUVA and not at Columbia, and the present invention compared vital signmetrics examined in this study with the HRC index in UVA patients only.

The summary statistics and logistic regression were used to describe andcompare vital signs collected from infants at UVA and Columbia.Univariate logistic regression analyses were performed to determinewhether there was a significant change in each vital sign metric in the24 hour period before diagnosis of LOS or NEC, compared to normativedata from all VLBW infants at all times. Demographic variablespotentially associated with LOS and NEC (gestational age, birthweight,gender, and postmenstrual age) were also analyzed. Bivariate analysesincorporating postmenstrual age and each vital sign metric wereperformed. Multivariate logistic regression models were developed usingdata from each site separately as the training set in an iterativeprocess with external validation on data from the other site.

For summary statistics, mean (standard deviation) is shown unlessotherwise indicated. For associations between vital signs and illnesses,Wald Chi-square and p values are reported, and for modeling, area underreceiver operator characteristics curve (ROC AUC) and 95% confidenceintervals are reported. The net reclassification improvement (NRI) wasalso used to compare the performance of the new vital sign models withthe HRC index. NRI is a measure of the fraction of cases that arecorrectly reclassified by a new risk assessment tool compared to anestablished tool (See Leening M J G, Vedder M M, Witteman J C M, PencinaM J, Steyerberg E W. Net Reclassification Improvement: Computation,Interpretation, and Controversies. Ann Intern Med 2014;160:122-31).Analyses were performed in MATLAB (MathWorks, Natick Mass.).

Of 1125 VLBW infants admitted to the two NICUs during the study period,vital sign data were available for 1065 (757 in 64 months at UVA and 308in 18 months at Columbia). The gestational age and birth weight weresimilar at the two institutions (UVA: 27.6±2.9 weeks and 1003±297 grams;Columbia: 28.5±3.2 weeks and 1030±313, grams). The total number ofinfant-years' vital sign data available for analysis was 95 and 36 forUVA and Columbia infants.

Among the 1065 infants, there were 123 cases of LOS and 63 cases of NECwith vital sign data available around the time of illness. The meangestational age and birth weight of infants with LOS or NEC were 25.9weeks and 817 grams, significantly lower than infants without theseillnesses. The organism distribution for the LOS cases was 88 (72%)Gram-positive, 34 (28%) Gram-negative or multiple organisms, and oneCandida species. The demographics and organisms for the LOS and NECcases were similar at the two institutions. The infants with LOS weremore likely to be on mechanical ventilation at the time of diagnosis(65/123, 53%) compared to infants with NEC (13/65, 21%), and infants atUVA were more likely to be on mechanical ventilation at the time of LOSor NEC (64/121, 53%) compared to infants at Columbia (14/65, 22%).

The mean, standard deviation and cross-correlation of HR, RR, and SpO2were analyzed during all times data were available. The total number ofhours of data analyzed was 1.15 million (130.9 infant-years), and thebreakdown by institution was UVA, 0.84M (95.4 infant-years) and Columbia0.31M (35.5 infant-years).

FIG. 3 summarizes demographic variables and mean vital signs for theentire NICU stay for all 1065 infants, and vital signs in the 24 hourperiod before diagnosis of either LOS or NEC. Mean post-menstrual age(PMA) at the time of illness was 30.4 weeks. In univariate analysis, thebest predictor of LOS or NEC being diagnosed within 24 hours was PMA. Inmultivariate analysis adjusting for PMA, cross correlation of HR-SpO2had the highest ROC area for LOS or NEC (0.733, p<0.001).

FIG. 4 shows means of each vital sign-related parameter in the 2 dayperiod before and after LOS or NEC diagnosis compared to the populationmean for all infants for the entire NICU stay (horizontal gray line).Generally, mean HR increased slightly and mean SpO2 decreased slightlyaround the time of diagnosis. Mean RR did not increase prior todiagnosis but its standard deviation did, which may reflect infantshaving more fluctuations between tachypnea and apnea. The vital signparameters changed after diagnosis, possibly related to therapeuticinterventions such as increased respiratory support. Of note, changes invital sign measures in the day prior to illness differed betweencenters. For example, there was a small, but statistically significantdecrease in mean SpO2 and increase in mean HR at UVA and not atColumbia, whereas SD of SpO2 changed before illness at Columbia and notat UVA. Importantly, though, the cross-correlation of HR-SpO2, whichmeasures co-trending of the two vital signs rather than their absolutevalues, was the best single predictor of illness at both centers.

FIG. 5 shows center-specific vital sign model ability to discriminateinfants with impending LOS or NEC. For modeling, the UVA training setconsisted of 825,493 hourly measurements with an event rate of 0.0032and the Columbia training set consisted of 235,458 hourly measurementswith an event rate of 0.0035. The contribution of each vital sign to the9-variable model is represented as a Chi-squared value and correspondingcoefficient p value. The performance and confidence intervals of the9-variable model and a model using the 3 best independent predictors(mean SpO2, SD HR, and cross-correlation HR-SpO2) is shown. The tableshows results of training and testing the model at each site separatelyand combined. The present invention also performed externalcross-validation. The UVA 3-variable model had an AUC of 0.754 whentested on Columbia data and the Columbia model had AUC 0.680 tested onUVA data. The 9-variable model had slightly less external validatedperformance with corresponding AUCs of 0.727 and 0.674.

FIG. 6 shows that the distribution of cross-correlation of HR-SpO2measurements was skewed toward higher values in the 24 h period prior toLOS or NEC diagnosis compared to the values of all infants at all times.The rise in cross correlation HR-SpO2 was greater in cases of NEC thanLOS and continued to rise after diagnosis (FIG. 7). The effect was seenin infants at both institutions (FIG. 8) and in infants on or offmechanical ventilation at the time of diagnosis (See FIGS. 9A-H).Combining LOS and NEC cases and both institutions, mean crosscorrelation of HR-SpO2 increased from 0.15 in the 24-48 h period priorto diagnosis to 0.21 from 0-24 h prior (p<0.001).

In 33% of the LOS or NEC events (61/186), at least one of the hours inthe day prior to the event had an extremely high cross-correlation >0.6corresponding to the tail of the distribution in FIG. 6. The presentinvention speculated that, in some cases, this finding represents apneaor periodic breathing with associated decline in HR and SpO2, and thepresent inventors reviewed respiratory rate, HR, and SpO2 patterns theday prior to illness diagnosis in all cases with very highcross-correlation.

Representative examples are shown. In some cases there was clearlycentral apnea associated with decline in HR and SpO2 deep enough to beconsidered “bradycardia/desaturation” by standard definitions (See FinerN N, Higgins R, Kattwinkel J, Martin R J. Summary proceedings from theapnea-of-prematurity group. Pediatrics 2006;117:S47-51). In others, theconcurrent fall and rise in HR, SpO2, and RR were of lower magnitude andduration but higher frequency, suggestive of periodic breathing withentrainment of HR and SpO2.

An HRC index (HeRO) monitor was in use at UVA during the period ofstudy, and scores were available for 620,978 of the hourly measurementsused in the UVA vital sign training set. For this subset, the additionalvalue of the parsimonious 3-parameter vital sign model to the HRC indexwere evaluated using logistic regression models and the netreclassification improvement (NRI) statistic.(12) The AUC on this subsetof data for the vital sign model and HRC index alone were 0.684 and0.707 respectively.

Combining the 3-variable model with the HRC index increased AUC by 0.021to 0.728 (95% confidence interval 0.010, 0.047 Wald chi-square=22.6,p=0.00001). The Net Reclassification Improvement for the vital signmodel was also highly significant with a value of 0.205 (0.113, 0.328).The cross correlation HR-SpO2 by itself also added significantly to theHRC index (Wald chi-square=9.14, p=0.01) with combined AUC of 0.715.This analysis demonstrates the potential of additional vital signanalyses to improve the sensitivity over the established heart ratecharacteristics index monitor for early detection of LOS and NEC.

The present inventors previously developed a monitor displaying a heartrate characteristics index as an early warning system for sepsis, and inthe current study analyzed not only HR, but also respiratory rate andSpO2. The present inventors further expanded the study by analyzing datafrom a large number of VLBW infants at two institutions and in twoillnesses. The major finding is that, while the value of individualvital signs for detection of LOS and NEC differed across institutions,an increase in cross-correlation of HR-SpO2 performed well in both unitsand added to the HRC index for early detection of illness.

High cross correlation of HR-SpO2 may reflect apnea in infants who arenot on mechanical ventilation. An acute increase in central apnea is oneof the most common signs of late-onset septicemia in preterm infants inthe NICU (See Das A, Shukla S, Rahman N, Gunzler D, Abughali N. ClinicalIndicators of Late-Onset Sepsis Workup in Very Low-Birth-Weight Infantsin the Neonatal Intensive Care Unit. Am J Perinatol 2016) and apnea isoften accompanied by both bradycardia and oxygen desaturation. Periodicbreathing, alternating brief apneic pauses and breaths, is normal inneonates and sometimes associated with decline in HR and SpO2.Preclinical studies indicate that cytokines and prostaglandins releasedas part of the systemic inflammatory response are responsible foremergence of immature breathing patterns during illness (See HofstetterA O, Saha S, Siljehav V, Jakobsson P-J, Herlenius E. The inducedprostaglandin E2 pathway is a key regulator of the respiratory responseto infection and hypoxia in neonates. Proc Natl Acad Sci USA2007;104:9894-9; and Balan K V., Kc P, Hoxha Z, Mayer C A, Wilson C G,Martin R J. Vagal afferents modulate cytokine-mediated respiratorycontrol at the neonatal medulla oblongata. Respir Physiol Neurobiol2011;178:458-64) and the present inventors have previously reported thatsome preterm infants exhibit an acute increase in periodic breathing orin central “ABDs” (apnea with both bradycardia and desaturation) in theday before they are diagnosed with LOS or NEC (See Patel M, Mohr M, LakeD, et al. Clinical Associations with Immature Breathing in PretermInfants. Part 2: Periodic Breathing. Pediatr Res 2016; and Fairchild K,Mohr M, Paget-Brown A, et al. Clinical associations of immaturebreathing in preterm infants: part 1-central apnea. Pediatr Res 2016).Quantitation of central apnea requires storage and analysis of largedata files of chest impedance waveforms which is difficult to implementbroadly, and in the current study we sought simpler measures using vitalsigns and their interactions. On reviewing examples of very highcross-correlation of HR-SpO2, it was found that many were clearlyassociated with decline in breathing rate consistent with central apneaand periodic breathing, and further work is needed to substantiate thisassociation. Interestingly, some infants on mechanical ventilation alsohad high cross correlation of HR-SpO2 at the time of LOS or NECdiagnosis, indicating that there is more at play in the pathophysiologythan central apnea. The present invention speculates that some of thismay reflect autonomic nervous system activation or dysfunction, oraltered vasoreactivity as part of a systemic inflammatory response.

In order for predictive models to be widely applicable it is importantthat they be developed and tested at different institutions. It wasfound small but statistically significant differences in individualvital signs of infants exist at University of Virginia (UVA) andColumbia University (CU), most notably, mean and standard deviation ofSpO2. This may be related to differences in clinical managementstrategies of lung disease or apnea, and may account for the more robustperformance of vital sign interactions such as cross-correlation ofHR-SpO2 across institutions, since this measure reflects co-trendingrather than absolute values of vital signs.

Any new diagnostic or predictive test such as cross correlation ofHR-SpO2 should be compared to existing modalities. The HRC index waspreviously developed by our group as an early warning system for sepsisand is displayed in the UVA NICU. The present inventors found that crosscorrelation of HR-SpO2 and the 3-vital sign model incorporating meanSpO2, SD HR, and cross correlation of HR-SpO2 had slightly lower ROCarea compared to the HRC index and that there was additive value incombining these measures to improve the sensitivity for early detectionof LOS and NEC. More work is required to determine whether displayingone or more scores representing multiple vital sign patterns willfacilitate earlier detection of illnesses and improve outcomes ofinfants in the NICU.

Sepsis and NEC continue to contribute a great deal to morbidity andmortality of preterm infants (See Stoll B J, Hansen N I, Bell EF, et al.Trends in Care Practices, Morbidity, and Mortality of Extremely PretermNeonates, 1993-2012. JAMA 2015;314:1039-51). Detection and treatment inthe early phase of illness, before overt clinical deterioration, islikely to improve outcomes but is difficult due to the subtlety of theearly physiologic changes. Analysis of multiple vital signs and theirinteractions can assist in preclinical detection in some cases, andtranslating these metrics to real-time bedside displays and testingtheir impact on outcomes in randomized clinical trials is an essentialnext step.

In 186 cases of LOS or NEC at UVA and Columbia, mean, standard deviationand cross-correlation of heart rate (HR), respiratory rate (RR), andoxygen saturation (SpO2) are shown 5 days before and after diagnosis.Mean (solid line) and standard deviation (dotted line) are shown. Thehorizontal dashed line represents the value for all VLBW infants at alltimes.

Vital sign data was analyzed for cross-correlation of HR-SpO2 for 1065VLBW infants at all times and around the time of 123 cases of late-onsetsepticemia (LOS) and 63 cases of NEC. A) For each cross-correlationvalue, the density of hourly measurements for all 1065 VLBW infants atall times during the NICU stay is shown by the grey dashed line and thenumber of measurements in the 24h period prior to illness diagnosis bythe black line. B-D) Mean cross-correlation of HR-SpO2 2 days prior toand following diagnosis of illness. Increased cross-correlation occurredin both illnesses (B, LOS solid line, versus NEC dotted line), in bothinstitutions (C, UVA solid line, versus Columbia dotted line), and ininfants on or off mechanical ventilation (D, on ventilator solid line,off ventilator dotted line).

Representative one hour tracing of HR and SpO2 (top), and 10 minutetracings of HR, SpO2, and respiratory rate (insets at bottom) are shownfor 2 infants in the day prior to diagnosis of illness. A) UVA infant 5hours prior to diagnosis of sepsis, when cross-correlation of HR-SpO2was 0.844. There are frequent HR decelerations (solid black line) andconcurrent decline in SpO2 (dotted black line), preceded by decline inrespiratory rate (solid grey line in bottom inset, note right Y axis forrespiratory rate) B) Columbia infant 1.5 hours prior to diagnosis ofNEC, when cross-correlation of HR-SpO2 was 0.81. There are repetitive,regular declines in HR and SpO2 associated with decline in respiratoryrate.

It should also be appreciated that the exact manner of obtaining vitalsigns and measuring the levels of one or more biochemical substances andthe subsequent analysis can be accomplished by any number of techniques.For example, it may be achieved by the common paradigm whereby vitalsigns and samples are taken in person and the vital signs samples areanalyzed locally or are physically transferred to other facilities wherethey can be tested and analyzed. However, it may also be achieved byincorporating a “telemedicine” paradigm whereby, at one or more pointsduring the process, information is transferred over a wired or wirelessdata communications network to a remote location where subsequentanalysis or other processing may take place. For example, an aspect ofembodiment of the invention may involve electronically transferring theresults of vital signs and sample measurement (E.g., bedside vital signdata) over a data communications network to a remote location wheresubsequent assessment and/or analysis can take place. Such utilizationof telecommunications networks may occur during any step in the processand may be utilized at a single or multiple points. Likewise,telecommunications networks may be incorporated into any part of thesystem.

Furthermore, information can be displayed at any point during theprocess, or at any point in the system, in any number of ways. Forexample, readings and data may be received and/or displayed by the user,clinician, physician, technician, patient or the like by hard copy(e.g., paper), visual graphics, audible signals (such as voice or tones,for example), or any combination thereof. Additionally, anymeasurements, assessment, analysis, secondary information, diagnosis,reading, data, or discussion may be reduced to hard copy (e.g., paper)or computer storage medium at any point during the process (or system).

FIG. 10 illustrates a block diagram of an example machine 400 upon whichone or more embodiments (e.g., discussed methodologies) can beimplemented (e.g., run).

Examples of machine 400 can include logic, one or more components,circuits (e.g., modules), or mechanisms. Circuits are tangible entitiesconfigured to perform certain operations. In an example, circuits can bearranged (e.g., internally or with respect to external entities such asother circuits) in a specified manner. In an example, one or morecomputer systems (e.g., a standalone, client or server computer system)or one or more hardware processors (processors) can be configured bysoftware (e.g., instructions, an application portion, or an application)as a circuit that operates to perform certain operations as describedherein. In an example, the software can reside (1) on a non-transitorymachine readable medium or (2) in a transmission signal. In an example,the software, when executed by the underlying hardware of the circuit,causes the circuit to perform the certain operations.

In an example, a circuit can be implemented mechanically orelectronically. For example, a circuit can comprise dedicated circuitryor logic that is specifically configured to perform one or moretechniques such as discussed above, such as including a special-purposeprocessor, a field programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC). In an example, a circuitcan comprise programmable logic (e.g., circuitry, as encompassed withina general-purpose processor or other programmable processor) that can betemporarily configured (e.g., by software) to perform the certainoperations. It will be appreciated that the decision to implement acircuit mechanically (e.g., in dedicated and permanently configuredcircuitry), or in temporarily configured circuitry (e.g., configured bysoftware) can be driven by cost and time considerations.

Accordingly, the term “circuit” is understood to encompass a tangibleentity, be that an entity that is physically constructed, permanentlyconfigured (e.g., hardwired), or temporarily (e.g., transitorily)configured (e.g., programmed) to operate in a specified manner or toperform specified operations. In an example, given a plurality oftemporarily configured circuits, each of the circuits need not beconfigured or instantiated at any one instance in time. For example,where the circuits comprise a general-purpose processor configured viasoftware, the general-purpose processor can be configured as respectivedifferent circuits at different times. Software can accordinglyconfigure a processor, for example, to constitute a particular circuitat one instance of time and to constitute a different circuit at adifferent instance of time.

In an example, circuits can provide information to, and receiveinformation from, other circuits. In this example, the circuits can beregarded as being communicatively coupled to one or more other circuits.Where multiple of such circuits exist contemporaneously, communicationscan be achieved through signal transmission (e.g., over appropriatecircuits and buses) that connect the circuits. In embodiments in whichmultiple circuits are configured or instantiated at different times,communications between such circuits can be achieved, for example,through the storage and retrieval of information in memory structures towhich the multiple circuits have access. For example, one circuit canperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further circuit canthen, at a later time, access the memory device to retrieve and processthe stored output. In an example, circuits can be configured to initiateor receive communications with input or output devices and can operateon a resource (e.g., a collection of information).

The various operations of method examples described herein can beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors can constitute processor-implementedcircuits that operate to perform one or more operations or functions. Inan example, the circuits referred to herein can compriseprocessor-implemented circuits.

Similarly, the methods described herein can be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod can be performed by one or processors or processor-implementedcircuits. The performance of certain of the operations can bedistributed among the one or more processors, not only residing within asingle machine, but deployed across a number of machines. In an example,the processor or processors can be located in a single location (e.g.,within a home environment, an office environment or as a server farm),while in other examples the processors can be distributed across anumber of locations.

The one or more processors can also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations can be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., Application Program Interfaces (APIs).)

Example embodiments (e.g., apparatus, systems, or methods) can beimplemented in digital electronic circuitry, in computer hardware, infirmware, in software, or in any combination thereof. Exampleembodiments can be implemented using a computer program product (e.g., acomputer program, tangibly embodied in an information carrier or in amachine readable medium, for execution by, or to control the operationof, data processing apparatus such as a programmable processor, acomputer, or multiple computers).

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a software module,subroutine, or other unit suitable for use in a computing environment. Acomputer program can be deployed to be executed on one computer or onmultiple computers at one site or distributed across multiple sites andinterconnected by a communication network.

In an example, operations can be performed by one or more programmableprocessors executing a computer program to perform functions byoperating on input data and generating output. Examples of methodoperations can also be performed by, and example apparatus can beimplemented as, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)).

The computing system can include clients and servers. A client andserver are generally remote from each other and generally interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware can be a designchoice. Below are set out hardware (e.g., machine 400) and softwarearchitectures that can be deployed in example embodiments.

In an example, the machine 400 can operate as a standalone device or themachine 400 can be connected (e.g., networked) to other machines.

In a networked deployment, the machine 400 can operate in the capacityof either a server or a client machine in server-client networkenvironments. In an example, machine 400 can act as a peer machine inpeer-to-peer (or other distributed) network environments. The machine400 can be a personal computer (PC), a tablet PC, a set-top box (STB), aPersonal Digital Assistant (PDA), a mobile telephone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions (sequential or otherwise) specifying actions to be taken(e.g., performed) by the machine 400. Further, while only a singlemachine 400 is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

Example machine (e.g., computer system) 400 can include a processor 402(e.g., a central processing unit (CPU), a graphics processing unit (GPU)or both), a main memory 404 and a static memory 406, some or all ofwhich can communicate with each other via a bus 408. The machine 400 canfurther include a display unit 410, an alphanumeric input device 412(e.g., a keyboard), and a user interface (UI) navigation device 411(e.g., a mouse). In an example, the display unit 810, input device 417and UI navigation device 414 can be a touch screen display. The machine400 can additionally include a storage device (e.g., drive unit) 416, asignal generation device 418 (e.g., a speaker), a network interfacedevice 420, and one or more sensors 421, such as a global positioningsystem (GPS) sensor, compass, accelerometer, or other sensor.

The storage device 416 can include a machine readable medium 422 onwhich is stored one or more sets of data structures or instructions 424(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 424 canalso reside, completely or at least partially, within the main memory404, within static memory 406, or within the processor 402 duringexecution thereof by the machine 400. In an example, one or anycombination of the processor 402, the main memory 404, the static memory406, or the storage device 416 can constitute machine readable media.

While the machine readable medium 422 is illustrated as a single medium,the term “machine readable medium” can include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) that configured to store the one or moreinstructions 424. The term “machine readable medium” can also be takento include any tangible medium that is capable of storing, encoding, orcarrying instructions for execution by the machine and that cause themachine to perform any one or more of the methodologies of the presentdisclosure or that is capable of storing, encoding or carrying datastructures utilized by or associated with such instructions. The term“machine readable medium” can accordingly be taken to include, but notbe limited to, solid-state memories, and optical and magnetic media.Specific examples of machine readable media can include non-volatilememory, including, by way of example, semiconductor memory devices(e.g., Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 424 can further be transmitted or received over acommunications network 426 using a transmission medium via the networkinterface device 420 utilizing any one of a number of transfer protocols(e.g., frame relay, IP, TCP, UDP, HTTP, etc.). Example communicationnetworks can include a local area network (LAN), a wide area network(WAN), a packet data network (e.g., the Internet), mobile telephonenetworks (e.g., cellular networks), Plain Old Telephone (POTS) networks,and wireless data networks (e.g., IEEE 802.11 standards family known asWi-Fi®, IEEE 802.16 standards family known as WiMax®), peer-to-peer(P2P) networks, among others. The term “transmission medium” shall betaken to include any intangible medium that is capable of storing,encoding or carrying instructions for execution by the machine, andincludes digital or analog communications signals or other intangiblemedium to facilitate communication of such software.

Pulse Oximetry Warning Scores “Pows” for Sepsis and Nec Prediction IVLBW Infants at Three (3) NICUs

The pulse oximetry warning score “POWS” for sepsis and NEC prediction inVLBW infants at three (3) NICUs is shown in FIG. 11.

The details of the background, objectives, and methods are set forth inFIG. 11.

The graph of average POWS verses Hours to event for the three (3) NICUsis shown in FIG. 11. This graph shows the results for predicting sepsisand NEC in VLBW infants. The NICU 3 results are compared to the controlfrom combined cohort 1 and 2.

The graph of Relative Risk verses Max Cross correlation PR-SpO₂ is shownin FIG. 11. The relative risk of LOS/NEC diagnosis within 24 h based onmaximum cross correlation of PR-SpO₂ is calculated using an empiricalrisk model vs linear regression model with non-linear transformation toimprove fit. The POWS AUC increased to 0.735 using this and othernon-linear transformation of input features.

The graph of Density verses Relative Risk is shown in FIG. 11. Thisgraph shows that within 24 hours prior to LOS/NEC events, the POWsvalues were higher compared to control values (all infants all times).

Further, the POWs AUC increased to 0.752 with the addition of skewnessof PR, reflecting heart rate decelerations.

In conclusion, pulse oximetry derived warning scores (POWS) predict LOSand NEC diagnosis within 24 hours across the multiple NICUs and performssimilarly to an algorithm using heart rate from ECG.

Heart Rate and Oxygen Saturation Cross-Correlation in Preterm Infants:Association with Apnea and Adverse Events

The heart rate and oxygen saturation cross-correlation in preterminfants and association with apnea and adverse event is shown in FIG.12.

The details of the background, objective, and methods are set forth inFIG. 12.

The graph of XCorr-HR-SpO₂ verses Weeks after birth and the graph of #ABD events per day verses XCorr-HR-SpO₂ are shown in FIG. 12. IncreasingXCorr-HR-SpO₂ is associated with ABDs. 49% of days with extremely highXCorr-HR-SpO₂ (>0.7) were associated with an adverse event.

The results show in 100 cases of sepsis/NEC, there was a mean 67%increase in XCorr-HR-SpO₂ in the 24 hour period prior to diagnosiscompared to the baseline. The heat map of XCorr-HR-SpO₂ and plot ofHR-SpO₂ 24 hours prior to diagnosis of sepsis is shown in FIG. 12.

In conclusion, increasing XCorr-HR-SpO₂ is associated with apnea withdeceleration-desaturation, and with adverse events including sepsis andNEC. Further, incorporating XCorr-HR-SpO₂ into predictive algorithms mayimprove on heart rate characteristics monitoring (HeRO) for sepsis earlywarning systems in the NICU.

Pulse Oximetry Data Predicting Risk of Death and Adverse Outcomes inVLBW Infants

The pulse oximetry data predicting risk of death and adverse outcomes inVLBW infants is shown in FIG. 13.

The details of the background, objective, and methods are shown in FIG.13. The results, including death, sIVH, BPD, tROP, LOS, and NEC areshown in FIG. 13.

In conclusion, pulse oximetry-derived pulse rate and SpO₂ analysisimproves risk prediction of morbidity and mortality in VLBW infants overdemographic risk factors alone.

Definitions

The definitions for acronyms used through the application are asfollows:

ABD apnea with both bradycardia and desaturation

API application program interface

ASIC application-specific integrated circuit

AUC area under the curve

BPD bronchopulmonary dysplasia

BW birth weight

CD-ROM compact disk read-only memory

COLUMBIA Columbia University

CPU central processing unit

CU Columbia University

DVD-ROM digital optical disk read-only memory

ECG electrocardiogram

EEPROM electrically erasable programmable read-only memory

EPROM programmable read-only memory

FPGA field programmable gate array

GA gestational age

GPS global positioning system

GPU graphic processing unit

HR heart rate

HTTP hypertext transfer protocol

IP internet protocol

HRC heart rate characteristics (index)

IVH intraventricular hemorrhage

LAN local area network

LOS late-onset septicemia

NEC necrotizing enterocolitis

NICU neonatal Intensive Care Unit

NIH National Institute of Health

NRI net reclassification improvement

PC personal computer

PDA personal digital assistant

POPS pulse oximetry predictive scores

POTS plain old telephone service

ROC receiver operating characteristic

ROP retinopathy of prematurity

RR respiratory rate

SD standard deviation

SpO2 peripheral capillary oxygen saturation

STB set-top box

TCP transmission control protocol

UDP user datagram protocol

UI user interface

UVA University of Virginia

VLBW very low birth weight (infants)

WAN wide area network

In summary, while the present invention has been described with respectto specific embodiments, many modifications, variations, alterations,substitutions, and equivalents will be apparent to those skilled in theart. The present invention is not to be limited in scope by the specificembodiment described herein. Indeed, various modifications of thepresent invention, in addition to those described herein, will beapparent to those of skill in the art from the foregoing description andaccompanying drawings. Accordingly, the invention is to be considered aslimited only by the spirit and scope of the disclosure, including allmodifications and equivalents.

Still other embodiments will become readily apparent to those skilled inthis art from reading the above-recited detailed description anddrawings of certain exemplary embodiments. It should be understood thatnumerous variations, modifications, and additional embodiments arepossible, and accordingly, all such variations, modifications, andembodiments are to be regarded as being within the spirit and scope ofthis application. For example, regardless of the content of any portion(e.g., title, field, background, summary, abstract, drawing figure,etc.) of this application, unless clearly specified to the contrary,there is no requirement for the inclusion in any claim herein or of anyapplication claiming priority hereto of any particular described orillustrated activity or element, any particular sequence of suchactivities, or any particular interrelationship of such elements.Moreover, any activity can be repeated, any activity can be performed bymultiple entities, and/or any element can be duplicated. Further, anyactivity or element can be excluded, the sequence of activities canvary, and/or the interrelationship of elements can vary. Unless clearlyspecified to the contrary, there is no requirement for any particulardescribed or illustrated activity or element, any particular sequence orsuch activities, any particular size, speed, material, dimension orfrequency, or any particularly interrelationship of such elements.Accordingly, the descriptions and drawings are to be regarded asillustrative in nature, and not as restrictive. Moreover, when anynumber or range is described herein, unless clearly stated otherwise,that number or range is approximate. When any range is described herein,unless clearly stated otherwise, that range includes all values thereinand all sub ranges therein. Any information in any material (e.g., aUnited States/foreign patent, United States/foreign patent application,book, article, etc.) that has been incorporated by reference herein, isonly incorporated by reference to the extent that no conflict existsbetween such information and the other statements and drawings set forthherein. In the event of such conflict, including a conflict that wouldrender invalid any claim herein or seeking priority hereto, then anysuch conflicting information in such incorporated by reference materialis specifically not incorporated by reference herein.

1. A method for generating pulse oximetry predictive scores (POPS) forpredicting adverse neurological development in preterm infants,comprising measuring mean and standard deviation of HR and SpO2;measuring cross-correlation of HR and SpO2; measuring HR decelerations;measuring HR and SpO2 entropy; measuring hypoxia and hyperoxia;analyzing the measured data; and generating pulse oximetry predictivescores using the analyzed data; wherein these measurements are made overdifferent time periods specific to a pathology being predicted.
 2. Themethod according to claim 1, wherein the time periods are based on deathor IVH at first 24 hour or shorter time periods, after birth.
 3. Themethod according to claim 1, wherein the time periods are based onsepsis or NEC at 2 days leading up to clinical diagnosis, includingincrease over patient baseline or increase over population normal. 4.The method according to claim 1, wherein the time periods are based onBPD or ROP at first day, week, or month.
 5. The method according toclaim 1, wherein the time periods are based on prolonged NICU stay atfirst day, week, or month.
 6. The method according to claim 1, includinggenerating an algorithm using gestational age, birth weight, andpost-menstrual age.
 7. The method according to claim 1, includingexploring specific laboratory values, including white blood cell count,hematocrit, and C-reactive protein.
 8. The method according to claim 1,including developing predictive scores using pulse oximeter-derivedheart rate (HR) and oxygen saturation (SpO2).
 9. The method according toclaim 8, including using standard demographic risk factors to determinerisk of early or late death or prolonged NICU stay.
 10. The methodaccording to claim 1, including monitoring changes in cardiorespiratorychanges occurring in the preterm infants with systemic inflammationrelated to late-onset septicemia (LOS) or necrotizing enterocolitis(NEC), and alerting clinicians before overt signs of illness emerges.11. The method according to claim 10, including monitoring respiratoryrate (RR), heart rate (HR), and oxygen saturation (SpO2) of the preterminfants.
 12. The method according to claim 1, including monitoringchanges in vital sign patterns of the preterm infants.
 13. The methodaccording to claim 12, wherein monitoring data is collected using aserver.
 14. The method according to claim 13, wherein the monitoringdata, includes respiration rate (RR) is derived from the chest impedancesignal, heart rate (HR) is derived from an ECG signal, and SpO2 isderived from a pulse oximeter, and the monitoring data is collectedevery 2 seconds.
 15. The method according to claim 14, wherein maximumcross-correlation between two vital sign signals is measured overten-minute windows by first standardizing each signal and then using aMatlab function XCORR, with a lag time of −30 to +30 seconds.